Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory538.1 MiB
Average record size in memory564.2 B

Variable types

Categorical6
Numeric15
Boolean1

Reproduction

Analysis started2025-08-11 13:42:37.084501
Analysis finished2025-08-11 13:47:22.136559
Duration4 minutes and 45.05 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Country
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.9 MiB
Russia
 
50532
South Africa
 
50408
South Korea
 
50181
Germany
 
50176
UK
 
50125
Other values (15)
748578 

Length

Max length12
Median length9
Mean length6.852176
Min length2

Characters and Unicode

Total characters6852176
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowItaly
2nd rowFrance
3rd rowTurkey
4th rowIndonesia
5th rowItaly

Common Values

ValueCountFrequency (%)
Russia 50532
 
5.1%
South Africa 50408
 
5.0%
South Korea 50181
 
5.0%
Germany 50176
 
5.0%
UK 50125
 
5.0%
Canada 50114
 
5.0%
Mexico 50080
 
5.0%
China 50066
 
5.0%
Nigeria 50046
 
5.0%
Saudi Arabia 49958
 
5.0%
Other values (10) 498314
49.8%

Length

2025-08-11T16:47:22.427803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 100589
 
8.7%
russia 50532
 
4.4%
africa 50408
 
4.4%
korea 50181
 
4.4%
germany 50176
 
4.4%
uk 50125
 
4.4%
canada 50114
 
4.4%
mexico 50080
 
4.4%
china 50066
 
4.4%
nigeria 50046
 
4.3%
Other values (12) 598230
52.0%

Most occurring characters

ValueCountFrequency (%)
a 1099842
16.1%
i 650048
 
9.5%
r 500051
 
7.3%
n 498931
 
7.3%
e 399881
 
5.8%
u 300933
 
4.4%
o 250606
 
3.7%
t 250179
 
3.7%
A 250030
 
3.6%
s 200773
 
2.9%
Other values (27) 2450902
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6852176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1099842
16.1%
i 650048
 
9.5%
r 500051
 
7.3%
n 498931
 
7.3%
e 399881
 
5.8%
u 300933
 
4.4%
o 250606
 
3.7%
t 250179
 
3.7%
A 250030
 
3.6%
s 200773
 
2.9%
Other values (27) 2450902
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6852176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1099842
16.1%
i 650048
 
9.5%
r 500051
 
7.3%
n 498931
 
7.3%
e 399881
 
5.8%
u 300933
 
4.4%
o 250606
 
3.7%
t 250179
 
3.7%
A 250030
 
3.6%
s 200773
 
2.9%
Other values (27) 2450902
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6852176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1099842
16.1%
i 650048
 
9.5%
r 500051
 
7.3%
n 498931
 
7.3%
e 399881
 
5.8%
u 300933
 
4.4%
o 250606
 
3.7%
t 250179
 
3.7%
A 250030
 
3.6%
s 200773
 
2.9%
Other values (27) 2450902
35.8%

Year
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.997
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:23.211384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2172866
Coefficient of variation (CV)0.0035871259
Kurtosis-1.2064316
Mean2011.997
Median Absolute Deviation (MAD)6
Skewness0.00067460415
Sum2.011997 × 109
Variance52.089226
MonotonicityNot monotonic
2025-08-11T16:47:23.503040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2007 40375
 
4.0%
2023 40274
 
4.0%
2000 40268
 
4.0%
2018 40228
 
4.0%
2003 40141
 
4.0%
2020 40130
 
4.0%
2016 40086
 
4.0%
2009 40079
 
4.0%
2002 40063
 
4.0%
2005 40039
 
4.0%
Other values (15) 598317
59.8%
ValueCountFrequency (%)
2000 40268
4.0%
2001 39896
4.0%
2002 40063
4.0%
2003 40141
4.0%
2004 40031
4.0%
2005 40039
4.0%
2006 40007
4.0%
2007 40375
4.0%
2008 39903
4.0%
2009 40079
4.0%
ValueCountFrequency (%)
2024 40003
4.0%
2023 40274
4.0%
2022 39876
4.0%
2021 40013
4.0%
2020 40130
4.0%
2019 39985
4.0%
2018 40228
4.0%
2017 39836
4.0%
2016 40086
4.0%
2015 39974
4.0%

Disease Name
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.5 MiB
COVID-19
 
50404
Zika
 
50313
Dengue
 
50289
Cancer
 
50285
HIV/AIDS
 
50274
Other values (15)
748435 

Length

Max length19
Median length9
Mean length8.490207
Min length4

Characters and Unicode

Total characters8490207
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalaria
2nd rowEbola
3rd rowCOVID-19
4th rowParkinson's Disease
5th rowTuberculosis

Common Values

ValueCountFrequency (%)
COVID-19 50404
 
5.0%
Zika 50313
 
5.0%
Dengue 50289
 
5.0%
Cancer 50285
 
5.0%
HIV/AIDS 50274
 
5.0%
Cholera 50249
 
5.0%
Asthma 50122
 
5.0%
Leprosy 50064
 
5.0%
Diabetes 50020
 
5.0%
Rabies 49975
 
5.0%
Other values (10) 498005
49.8%

Length

2025-08-11T16:47:23.815541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disease 99531
 
9.1%
covid-19 50404
 
4.6%
zika 50313
 
4.6%
dengue 50289
 
4.6%
cancer 50285
 
4.6%
hiv/aids 50274
 
4.6%
cholera 50249
 
4.6%
asthma 50122
 
4.6%
leprosy 50064
 
4.6%
diabetes 50020
 
4.5%
Other values (11) 547980
49.8%

Most occurring characters

ValueCountFrequency (%)
e 1047904
 
12.3%
s 847039
 
10.0%
a 799364
 
9.4%
i 648467
 
7.6%
r 399330
 
4.7%
l 399185
 
4.7%
o 398878
 
4.7%
n 398610
 
4.7%
D 300518
 
3.5%
t 249473
 
2.9%
Other values (31) 3001439
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8490207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1047904
 
12.3%
s 847039
 
10.0%
a 799364
 
9.4%
i 648467
 
7.6%
r 399330
 
4.7%
l 399185
 
4.7%
o 398878
 
4.7%
n 398610
 
4.7%
D 300518
 
3.5%
t 249473
 
2.9%
Other values (31) 3001439
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8490207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1047904
 
12.3%
s 847039
 
10.0%
a 799364
 
9.4%
i 648467
 
7.6%
r 399330
 
4.7%
l 399185
 
4.7%
o 398878
 
4.7%
n 398610
 
4.7%
D 300518
 
3.5%
t 249473
 
2.9%
Other values (31) 3001439
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8490207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1047904
 
12.3%
s 847039
 
10.0%
a 799364
 
9.4%
i 648467
 
7.6%
r 399330
 
4.7%
l 399185
 
4.7%
o 398878
 
4.7%
n 398610
 
4.7%
D 300518
 
3.5%
t 249473
 
2.9%
Other values (31) 3001439
35.4%

Disease Category
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.3 MiB
Metabolic
91332 
Parasitic
91178 
Genetic
91153 
Autoimmune
91153 
Neurological
91000 
Other values (6)
544184 

Length

Max length14
Median length11
Mean length9.364097
Min length5

Characters and Unicode

Total characters9364097
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRespiratory
2nd rowParasitic
3rd rowGenetic
4th rowAutoimmune
5th rowGenetic

Common Values

ValueCountFrequency (%)
Metabolic 91332
9.1%
Parasitic 91178
9.1%
Genetic 91153
9.1%
Autoimmune 91153
9.1%
Neurological 91000
9.1%
Cardiovascular 90968
9.1%
Viral 90910
9.1%
Infectious 90764
9.1%
Respiratory 90588
9.1%
Bacterial 90509
9.1%

Length

2025-08-11T16:47:24.112414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metabolic 91332
9.1%
parasitic 91178
9.1%
genetic 91153
9.1%
autoimmune 91153
9.1%
neurological 91000
9.1%
cardiovascular 90968
9.1%
viral 90910
9.1%
infectious 90764
9.1%
respiratory 90588
9.1%
bacterial 90509
9.1%

Most occurring characters

ValueCountFrequency (%)
i 1091178
11.7%
a 1000108
10.7%
r 817154
 
8.7%
e 727652
 
7.8%
c 727349
 
7.8%
o 727250
 
7.8%
t 636677
 
6.8%
l 545719
 
5.8%
u 455038
 
4.9%
n 363515
 
3.9%
Other values (20) 2272457
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9364097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1091178
11.7%
a 1000108
10.7%
r 817154
 
8.7%
e 727652
 
7.8%
c 727349
 
7.8%
o 727250
 
7.8%
t 636677
 
6.8%
l 545719
 
5.8%
u 455038
 
4.9%
n 363515
 
3.9%
Other values (20) 2272457
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9364097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1091178
11.7%
a 1000108
10.7%
r 817154
 
8.7%
e 727652
 
7.8%
c 727349
 
7.8%
o 727250
 
7.8%
t 636677
 
6.8%
l 545719
 
5.8%
u 455038
 
4.9%
n 363515
 
3.9%
Other values (20) 2272457
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9364097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1091178
11.7%
a 1000108
10.7%
r 817154
 
8.7%
e 727652
 
7.8%
c 727349
 
7.8%
o 727250
 
7.8%
t 636677
 
6.8%
l 545719
 
5.8%
u 455038
 
4.9%
n 363515
 
3.9%
Other values (20) 2272457
24.3%

Prevalence Rate (%)
Real number (ℝ)

Distinct1991
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.047992
Minimum0.1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:24.487457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.1
Q15.09
median10.04
Q315.01
95-th percentile19
Maximum20
Range19.9
Interquartile range (IQR)9.92

Descriptive statistics

Standard deviation5.7401893
Coefficient of variation (CV)0.57127727
Kurtosis-1.1988576
Mean10.047992
Median Absolute Deviation (MAD)4.96
Skewness0.00093135334
Sum10047992
Variance32.949774
MonotonicityNot monotonic
2025-08-11T16:47:24.846835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.87 600
 
0.1%
0.9 575
 
0.1%
17.37 573
 
0.1%
14.87 572
 
0.1%
7.1 569
 
0.1%
19.29 567
 
0.1%
1.47 567
 
0.1%
7.28 566
 
0.1%
8.79 566
 
0.1%
7.48 566
 
0.1%
Other values (1981) 994279
99.4%
ValueCountFrequency (%)
0.1 246
< 0.1%
0.11 539
0.1%
0.12 494
< 0.1%
0.13 511
0.1%
0.14 517
0.1%
0.15 504
0.1%
0.16 499
< 0.1%
0.17 489
< 0.1%
0.18 501
0.1%
0.19 528
0.1%
ValueCountFrequency (%)
20 255
< 0.1%
19.99 497
< 0.1%
19.98 513
0.1%
19.97 536
0.1%
19.96 468
< 0.1%
19.95 471
< 0.1%
19.94 495
< 0.1%
19.93 535
0.1%
19.92 509
0.1%
19.91 530
0.1%

Incidence Rate (%)
Real number (ℝ)

Distinct1491
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5550054
Minimum0.1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:25.206163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.85
Q13.84
median7.55
Q311.28
95-th percentile14.25
Maximum15
Range14.9
Interquartile range (IQR)7.44

Descriptive statistics

Standard deviation4.2989467
Coefficient of variation (CV)0.56901968
Kurtosis-1.1989126
Mean7.5550054
Median Absolute Deviation (MAD)3.72
Skewness-0.0001853991
Sum7555005.4
Variance18.480943
MonotonicityNot monotonic
2025-08-11T16:47:25.582849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.28 765
 
0.1%
10.72 751
 
0.1%
5.11 743
 
0.1%
1.53 743
 
0.1%
8.59 739
 
0.1%
5.08 738
 
0.1%
7.27 737
 
0.1%
10.48 737
 
0.1%
7.66 736
 
0.1%
4.47 736
 
0.1%
Other values (1481) 992575
99.3%
ValueCountFrequency (%)
0.1 331
< 0.1%
0.11 727
0.1%
0.12 630
0.1%
0.13 676
0.1%
0.14 679
0.1%
0.15 636
0.1%
0.16 706
0.1%
0.17 632
0.1%
0.18 636
0.1%
0.19 652
0.1%
ValueCountFrequency (%)
15 333
< 0.1%
14.99 682
0.1%
14.98 672
0.1%
14.97 694
0.1%
14.96 720
0.1%
14.95 637
0.1%
14.94 640
0.1%
14.93 681
0.1%
14.92 639
0.1%
14.91 647
0.1%

Mortality Rate (%)
Real number (ℝ)

Distinct991
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0499189
Minimum0.1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:26.010465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.59
Q12.58
median5.05
Q37.53
95-th percentile9.51
Maximum10
Range9.9
Interquartile range (IQR)4.95

Descriptive statistics

Standard deviation2.8594265
Coefficient of variation (CV)0.56623217
Kurtosis-1.2009075
Mean5.0499189
Median Absolute Deviation (MAD)2.48
Skewness0.00087853054
Sum5049918.9
Variance8.1763201
MonotonicityNot monotonic
2025-08-11T16:47:26.423700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.54 1121
 
0.1%
3.6 1101
 
0.1%
9.47 1095
 
0.1%
0.6 1091
 
0.1%
0.35 1090
 
0.1%
2.58 1087
 
0.1%
3.13 1086
 
0.1%
5.38 1082
 
0.1%
1.17 1080
 
0.1%
1.49 1078
 
0.1%
Other values (981) 989089
98.9%
ValueCountFrequency (%)
0.1 491
< 0.1%
0.11 1014
0.1%
0.12 1075
0.1%
0.13 1006
0.1%
0.14 992
0.1%
0.15 1025
0.1%
0.16 1008
0.1%
0.17 1030
0.1%
0.18 981
0.1%
0.19 1020
0.1%
ValueCountFrequency (%)
10 527
0.1%
9.99 1049
0.1%
9.98 982
0.1%
9.97 990
0.1%
9.96 1034
0.1%
9.95 1005
0.1%
9.94 1023
0.1%
9.93 1045
0.1%
9.92 995
0.1%
9.91 997
0.1%

Age Group
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.4 MiB
19-35
251201 
61+
249989 
0-18
249605 
36-60
249205 

Length

Max length5
Median length5
Mean length4.250417
Min length3

Characters and Unicode

Total characters4250417
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0-18
2nd row61+
3rd row36-60
4th row0-18
5th row61+

Common Values

ValueCountFrequency (%)
19-35 251201
25.1%
61+ 249989
25.0%
0-18 249605
25.0%
36-60 249205
24.9%

Length

2025-08-11T16:47:26.808034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-11T16:47:27.156211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
19-35 251201
25.1%
61 249989
25.0%
0-18 249605
25.0%
36-60 249205
24.9%

Most occurring characters

ValueCountFrequency (%)
1 750795
17.7%
- 750011
17.6%
6 748399
17.6%
3 500406
11.8%
0 498810
11.7%
9 251201
 
5.9%
5 251201
 
5.9%
+ 249989
 
5.9%
8 249605
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4250417
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 750795
17.7%
- 750011
17.6%
6 748399
17.6%
3 500406
11.8%
0 498810
11.7%
9 251201
 
5.9%
5 251201
 
5.9%
+ 249989
 
5.9%
8 249605
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4250417
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 750795
17.7%
- 750011
17.6%
6 748399
17.6%
3 500406
11.8%
0 498810
11.7%
9 251201
 
5.9%
5 251201
 
5.9%
+ 249989
 
5.9%
8 249605
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4250417
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 750795
17.7%
- 750011
17.6%
6 748399
17.6%
3 500406
11.8%
0 498810
11.7%
9 251201
 
5.9%
5 251201
 
5.9%
+ 249989
 
5.9%
8 249605
 
5.9%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.1 MiB
Male
333676 
Female
333223 
Other
333101 

Length

Max length6
Median length5
Mean length4.999547
Min length4

Characters and Unicode

Total characters4999547
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowOther
5th rowMale

Common Values

ValueCountFrequency (%)
Male 333676
33.4%
Female 333223
33.3%
Other 333101
33.3%

Length

2025-08-11T16:47:27.526591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-11T16:47:27.806685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 333676
33.4%
female 333223
33.3%
other 333101
33.3%

Most occurring characters

ValueCountFrequency (%)
e 1333223
26.7%
a 666899
13.3%
l 666899
13.3%
M 333676
 
6.7%
F 333223
 
6.7%
m 333223
 
6.7%
O 333101
 
6.7%
t 333101
 
6.7%
h 333101
 
6.7%
r 333101
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4999547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1333223
26.7%
a 666899
13.3%
l 666899
13.3%
M 333676
 
6.7%
F 333223
 
6.7%
m 333223
 
6.7%
O 333101
 
6.7%
t 333101
 
6.7%
h 333101
 
6.7%
r 333101
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4999547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1333223
26.7%
a 666899
13.3%
l 666899
13.3%
M 333676
 
6.7%
F 333223
 
6.7%
m 333223
 
6.7%
O 333101
 
6.7%
t 333101
 
6.7%
h 333101
 
6.7%
r 333101
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4999547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1333223
26.7%
a 666899
13.3%
l 666899
13.3%
M 333676
 
6.7%
F 333223
 
6.7%
m 333223
 
6.7%
O 333101
 
6.7%
t 333101
 
6.7%
h 333101
 
6.7%
r 333101
 
6.7%

Population Affected
Real number (ℝ)

Distinct632061
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500735.43
Minimum1000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:28.125566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile50742
Q1250491.25
median501041
Q3750782
95-th percentile950293
Maximum1000000
Range999000
Interquartile range (IQR)500290.75

Descriptive statistics

Standard deviation288660.12
Coefficient of variation (CV)0.57647233
Kurtosis-1.2021473
Mean500735.43
Median Absolute Deviation (MAD)250140
Skewness-0.0026304185
Sum5.0073543 × 1011
Variance8.3324663 × 1010
MonotonicityNot monotonic
2025-08-11T16:47:28.507059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600728 8
 
< 0.1%
325528 8
 
< 0.1%
868684 8
 
< 0.1%
428181 8
 
< 0.1%
200896 8
 
< 0.1%
261706 8
 
< 0.1%
992956 8
 
< 0.1%
13550 8
 
< 0.1%
126043 8
 
< 0.1%
719294 8
 
< 0.1%
Other values (632051) 999920
> 99.9%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1001 3
< 0.1%
1002 1
 
< 0.1%
1003 3
< 0.1%
1004 2
< 0.1%
1006 2
< 0.1%
1007 2
< 0.1%
1008 2
< 0.1%
1010 2
< 0.1%
1011 1
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
999999 2
< 0.1%
999998 3
< 0.1%
999995 2
< 0.1%
999994 1
 
< 0.1%
999993 2
< 0.1%
999991 2
< 0.1%
999990 1
 
< 0.1%
999989 1
 
< 0.1%
999988 1
 
< 0.1%

Healthcare Access (%)
Real number (ℝ)

Distinct5001
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.987835
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:28.859529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.48
Q162.47
median75
Q387.49
95-th percentile97.49
Maximum100
Range50
Interquartile range (IQR)25.02

Descriptive statistics

Standard deviation14.436345
Coefficient of variation (CV)0.19251583
Kurtosis-1.2008018
Mean74.987835
Median Absolute Deviation (MAD)12.51
Skewness-0.00035951017
Sum74987835
Variance208.40806
MonotonicityNot monotonic
2025-08-11T16:47:29.233061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.01 267
 
< 0.1%
61.7 256
 
< 0.1%
73.84 256
 
< 0.1%
66.44 255
 
< 0.1%
70.8 250
 
< 0.1%
79.88 248
 
< 0.1%
80.24 247
 
< 0.1%
97.75 246
 
< 0.1%
57.61 246
 
< 0.1%
64.82 245
 
< 0.1%
Other values (4991) 997484
99.7%
ValueCountFrequency (%)
50 127
< 0.1%
50.01 223
< 0.1%
50.02 204
< 0.1%
50.03 210
< 0.1%
50.04 181
< 0.1%
50.05 208
< 0.1%
50.06 214
< 0.1%
50.07 216
< 0.1%
50.08 203
< 0.1%
50.09 202
< 0.1%
ValueCountFrequency (%)
100 112
< 0.1%
99.99 221
< 0.1%
99.98 181
< 0.1%
99.97 184
< 0.1%
99.96 192
< 0.1%
99.95 229
< 0.1%
99.94 210
< 0.1%
99.93 197
< 0.1%
99.92 203
< 0.1%
99.91 189
< 0.1%

Doctors per 1000
Real number (ℝ)

Distinct451
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7479292
Minimum0.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:29.566251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.72
Q11.62
median2.75
Q33.87
95-th percentile4.77
Maximum5
Range4.5
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.2990666
Coefficient of variation (CV)0.47274383
Kurtosis-1.1992453
Mean2.7479292
Median Absolute Deviation (MAD)1.12
Skewness0.0022224586
Sum2747929.2
Variance1.687574
MonotonicityNot monotonic
2025-08-11T16:47:29.907609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.47 2358
 
0.2%
0.88 2352
 
0.2%
0.85 2352
 
0.2%
2.69 2338
 
0.2%
2.37 2335
 
0.2%
1.87 2333
 
0.2%
4.2 2330
 
0.2%
2.61 2328
 
0.2%
3.98 2316
 
0.2%
1.78 2316
 
0.2%
Other values (441) 976642
97.7%
ValueCountFrequency (%)
0.5 1132
0.1%
0.51 2190
0.2%
0.52 2245
0.2%
0.53 2202
0.2%
0.54 2265
0.2%
0.55 2230
0.2%
0.56 2261
0.2%
0.57 2306
0.2%
0.58 2241
0.2%
0.59 2203
0.2%
ValueCountFrequency (%)
5 1053
0.1%
4.99 2192
0.2%
4.98 2223
0.2%
4.97 2186
0.2%
4.96 2256
0.2%
4.95 2213
0.2%
4.94 2187
0.2%
4.93 2301
0.2%
4.92 2282
0.2%
4.91 2256
0.2%

Hospital Beds per 1000
Real number (ℝ)

Distinct951
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2459309
Minimum0.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:30.232977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.97
Q12.87
median5.24
Q37.62
95-th percentile9.52
Maximum10
Range9.5
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.7428651
Coefficient of variation (CV)0.52285575
Kurtosis-1.1995238
Mean5.2459309
Median Absolute Deviation (MAD)2.37
Skewness0.0013926455
Sum5245930.9
Variance7.5233092
MonotonicityNot monotonic
2025-08-11T16:47:30.596231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.22 1151
 
0.1%
2.9 1146
 
0.1%
0.8 1139
 
0.1%
1.62 1139
 
0.1%
0.82 1134
 
0.1%
3.82 1132
 
0.1%
3.26 1132
 
0.1%
0.65 1127
 
0.1%
8.67 1127
 
0.1%
6.39 1126
 
0.1%
Other values (941) 988647
98.9%
ValueCountFrequency (%)
0.5 556
0.1%
0.51 1035
0.1%
0.52 1047
0.1%
0.53 1091
0.1%
0.54 1097
0.1%
0.55 1053
0.1%
0.56 1069
0.1%
0.57 1053
0.1%
0.58 966
0.1%
0.59 1075
0.1%
ValueCountFrequency (%)
10 505
0.1%
9.99 1112
0.1%
9.98 1122
0.1%
9.97 1087
0.1%
9.96 1054
0.1%
9.95 1042
0.1%
9.94 1056
0.1%
9.93 1062
0.1%
9.92 1073
0.1%
9.91 1000
0.1%

Treatment Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.7 MiB
Surgery
250528 
Therapy
250263 
Vaccination
249753 
Medication
249456 

Length

Max length11
Median length7
Mean length8.74738
Min length7

Characters and Unicode

Total characters8747380
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedication
2nd rowSurgery
3rd rowVaccination
4th rowSurgery
5th rowMedication

Common Values

ValueCountFrequency (%)
Surgery 250528
25.1%
Therapy 250263
25.0%
Vaccination 249753
25.0%
Medication 249456
24.9%

Length

2025-08-11T16:47:30.928610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-11T16:47:31.229094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
surgery 250528
25.1%
therapy 250263
25.0%
vaccination 249753
25.0%
medication 249456
24.9%

Most occurring characters

ValueCountFrequency (%)
a 999225
11.4%
i 998418
11.4%
r 751319
 
8.6%
e 750247
 
8.6%
n 748962
 
8.6%
c 748962
 
8.6%
y 500791
 
5.7%
o 499209
 
5.7%
t 499209
 
5.7%
S 250528
 
2.9%
Other values (8) 2000510
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8747380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 999225
11.4%
i 998418
11.4%
r 751319
 
8.6%
e 750247
 
8.6%
n 748962
 
8.6%
c 748962
 
8.6%
y 500791
 
5.7%
o 499209
 
5.7%
t 499209
 
5.7%
S 250528
 
2.9%
Other values (8) 2000510
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8747380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 999225
11.4%
i 998418
11.4%
r 751319
 
8.6%
e 750247
 
8.6%
n 748962
 
8.6%
c 748962
 
8.6%
y 500791
 
5.7%
o 499209
 
5.7%
t 499209
 
5.7%
S 250528
 
2.9%
Other values (8) 2000510
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8747380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 999225
11.4%
i 998418
11.4%
r 751319
 
8.6%
e 750247
 
8.6%
n 748962
 
8.6%
c 748962
 
8.6%
y 500791
 
5.7%
o 499209
 
5.7%
t 499209
 
5.7%
S 250528
 
2.9%
Other values (8) 2000510
22.9%
Distinct49901
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25010.314
Minimum100
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:31.747288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile2593
Q112538
median24980
Q337493
95-th percentile47477
Maximum50000
Range49900
Interquartile range (IQR)24955

Descriptive statistics

Standard deviation14402.279
Coefficient of variation (CV)0.5758536
Kurtosis-1.2002315
Mean25010.314
Median Absolute Deviation (MAD)12477
Skewness0.0029354329
Sum2.5010314 × 1010
Variance2.0742565 × 108
MonotonicityNot monotonic
2025-08-11T16:47:32.112241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11248 41
 
< 0.1%
30847 41
 
< 0.1%
42094 41
 
< 0.1%
13091 41
 
< 0.1%
3365 39
 
< 0.1%
3650 39
 
< 0.1%
22522 39
 
< 0.1%
20889 39
 
< 0.1%
8023 39
 
< 0.1%
5880 38
 
< 0.1%
Other values (49891) 999603
> 99.9%
ValueCountFrequency (%)
100 24
< 0.1%
101 23
< 0.1%
102 12
< 0.1%
103 18
< 0.1%
104 19
< 0.1%
105 21
< 0.1%
106 27
< 0.1%
107 18
< 0.1%
108 19
< 0.1%
109 18
< 0.1%
ValueCountFrequency (%)
50000 17
< 0.1%
49999 23
< 0.1%
49998 15
< 0.1%
49997 22
< 0.1%
49996 22
< 0.1%
49995 14
< 0.1%
49994 33
< 0.1%
49993 19
< 0.1%
49992 19
< 0.1%
49991 23
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size976.7 KiB
True
500354 
False
499646 
ValueCountFrequency (%)
True 500354
50.0%
False 499646
50.0%
2025-08-11T16:47:32.410639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Recovery Rate (%)
Real number (ℝ)

Distinct4901
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.496934
Minimum50
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:32.727364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.45
Q162.22
median74.47
Q386.78
95-th percentile96.56
Maximum99
Range49
Interquartile range (IQR)24.56

Descriptive statistics

Standard deviation14.155168
Coefficient of variation (CV)0.19001008
Kurtosis-1.202015
Mean74.496934
Median Absolute Deviation (MAD)12.28
Skewness0.00063908002
Sum74496934
Variance200.36879
MonotonicityNot monotonic
2025-08-11T16:47:33.082417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.78 253
 
< 0.1%
64.69 251
 
< 0.1%
64.91 249
 
< 0.1%
84.72 249
 
< 0.1%
63.55 247
 
< 0.1%
86.21 246
 
< 0.1%
52.87 246
 
< 0.1%
65.92 246
 
< 0.1%
59.18 246
 
< 0.1%
94.08 245
 
< 0.1%
Other values (4891) 997522
99.8%
ValueCountFrequency (%)
50 106
< 0.1%
50.01 193
< 0.1%
50.02 210
< 0.1%
50.03 208
< 0.1%
50.04 204
< 0.1%
50.05 217
< 0.1%
50.06 231
< 0.1%
50.07 213
< 0.1%
50.08 196
< 0.1%
50.09 211
< 0.1%
ValueCountFrequency (%)
99 94
 
< 0.1%
98.99 199
< 0.1%
98.98 202
< 0.1%
98.97 197
< 0.1%
98.96 196
< 0.1%
98.95 187
< 0.1%
98.94 206
< 0.1%
98.93 202
< 0.1%
98.92 182
< 0.1%
98.91 244
< 0.1%

DALYs
Real number (ℝ)

Distinct5000
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2499.1448
Minimum1
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:33.414701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile250
Q11245
median2499
Q33750
95-th percentile4751
Maximum5000
Range4999
Interquartile range (IQR)2505

Descriptive statistics

Standard deviation1443.9238
Coefficient of variation (CV)0.57776716
Kurtosis-1.2012648
Mean2499.1448
Median Absolute Deviation (MAD)1252
Skewness0.0013297303
Sum2.4991448 × 109
Variance2084915.9
MonotonicityNot monotonic
2025-08-11T16:47:33.763698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4815 254
 
< 0.1%
1223 243
 
< 0.1%
1100 242
 
< 0.1%
4406 242
 
< 0.1%
1209 242
 
< 0.1%
1771 242
 
< 0.1%
2177 241
 
< 0.1%
1491 241
 
< 0.1%
2189 241
 
< 0.1%
785 241
 
< 0.1%
Other values (4990) 997571
99.8%
ValueCountFrequency (%)
1 203
< 0.1%
2 206
< 0.1%
3 215
< 0.1%
4 192
< 0.1%
5 197
< 0.1%
6 199
< 0.1%
7 213
< 0.1%
8 184
< 0.1%
9 203
< 0.1%
10 223
< 0.1%
ValueCountFrequency (%)
5000 184
< 0.1%
4999 186
< 0.1%
4998 182
< 0.1%
4997 180
< 0.1%
4996 205
< 0.1%
4995 221
< 0.1%
4994 202
< 0.1%
4993 182
< 0.1%
4992 191
< 0.1%
4991 175
< 0.1%
Distinct1001
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0025927
Minimum0
Maximum10
Zeros514
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:34.126161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.5
median5
Q37.51
95-th percentile9.5
Maximum10
Range10
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation2.8882983
Coefficient of variation (CV)0.57736029
Kurtosis-1.2018939
Mean5.0025927
Median Absolute Deviation (MAD)2.5
Skewness-0.00064547722
Sum5002592.7
Variance8.3422673
MonotonicityNot monotonic
2025-08-11T16:47:34.496673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.75 1114
 
0.1%
6.42 1090
 
0.1%
6.53 1088
 
0.1%
1.36 1083
 
0.1%
8.1 1080
 
0.1%
8.91 1080
 
0.1%
8.46 1079
 
0.1%
8.16 1078
 
0.1%
5.32 1076
 
0.1%
1.18 1075
 
0.1%
Other values (991) 989157
98.9%
ValueCountFrequency (%)
0 514
0.1%
0.01 1009
0.1%
0.02 990
0.1%
0.03 1010
0.1%
0.04 988
0.1%
0.05 992
0.1%
0.06 1029
0.1%
0.07 991
0.1%
0.08 985
0.1%
0.09 1010
0.1%
ValueCountFrequency (%)
10 507
0.1%
9.99 1034
0.1%
9.98 947
0.1%
9.97 943
0.1%
9.96 1002
0.1%
9.95 959
0.1%
9.94 976
0.1%
9.93 1003
0.1%
9.92 986
0.1%
9.91 1009
0.1%

Per Capita Income (USD)
Real number (ℝ)

Distinct99498
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50311.1
Minimum500
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:34.832332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile5485
Q125457
median50372
Q375195
95-th percentile95058.05
Maximum100000
Range99500
Interquartile range (IQR)49738

Descriptive statistics

Standard deviation28726.959
Coefficient of variation (CV)0.57098651
Kurtosis-1.1994281
Mean50311.1
Median Absolute Deviation (MAD)24867
Skewness-0.0029480415
Sum5.03111 × 1010
Variance8.2523819 × 108
MonotonicityNot monotonic
2025-08-11T16:47:35.180156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50248 26
 
< 0.1%
71449 26
 
< 0.1%
99172 26
 
< 0.1%
68896 25
 
< 0.1%
6659 25
 
< 0.1%
9123 25
 
< 0.1%
99504 25
 
< 0.1%
47901 25
 
< 0.1%
73463 24
 
< 0.1%
33285 24
 
< 0.1%
Other values (99488) 999749
> 99.9%
ValueCountFrequency (%)
500 10
< 0.1%
501 11
< 0.1%
502 15
< 0.1%
503 11
< 0.1%
504 13
< 0.1%
505 12
< 0.1%
506 12
< 0.1%
507 17
< 0.1%
508 9
< 0.1%
509 15
< 0.1%
ValueCountFrequency (%)
100000 12
< 0.1%
99999 16
< 0.1%
99998 13
< 0.1%
99997 6
 
< 0.1%
99996 12
< 0.1%
99995 13
< 0.1%
99994 11
< 0.1%
99993 10
< 0.1%
99992 7
< 0.1%
99991 13
< 0.1%

Education Index
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65006859
Minimum0.4
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:35.553193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.42
Q10.53
median0.65
Q30.78
95-th percentile0.88
Maximum0.9
Range0.5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.14447223
Coefficient of variation (CV)0.22224151
Kurtosis-1.1988075
Mean0.65006859
Median Absolute Deviation (MAD)0.13
Skewness-0.00047927829
Sum650068.59
Variance0.020872224
MonotonicityNot monotonic
2025-08-11T16:47:36.096981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67 20280
 
2.0%
0.57 20207
 
2.0%
0.86 20190
 
2.0%
0.85 20160
 
2.0%
0.82 20146
 
2.0%
0.88 20143
 
2.0%
0.87 20130
 
2.0%
0.43 20128
 
2.0%
0.73 20128
 
2.0%
0.41 20127
 
2.0%
Other values (41) 798361
79.8%
ValueCountFrequency (%)
0.4 9916
1.0%
0.41 20127
2.0%
0.42 19981
2.0%
0.43 20128
2.0%
0.44 20101
2.0%
0.45 19958
2.0%
0.46 20038
2.0%
0.47 19878
2.0%
0.48 19988
2.0%
0.49 20034
2.0%
ValueCountFrequency (%)
0.9 9922
1.0%
0.89 20040
2.0%
0.88 20143
2.0%
0.87 20130
2.0%
0.86 20190
2.0%
0.85 20160
2.0%
0.84 19925
2.0%
0.83 19980
2.0%
0.82 20146
2.0%
0.81 20074
2.0%

Urbanization Rate (%)
Real number (ℝ)

Distinct7001
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.985212
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-08-11T16:47:36.654577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23.5
Q137.47
median54.98
Q372.51
95-th percentile86.51
Maximum90
Range70
Interquartile range (IQR)35.04

Descriptive statistics

Standard deviation20.214042
Coefficient of variation (CV)0.36762688
Kurtosis-1.2003533
Mean54.985212
Median Absolute Deviation (MAD)17.52
Skewness0.0016124564
Sum54985212
Variance408.6075
MonotonicityNot monotonic
2025-08-11T16:47:37.042544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.79 188
 
< 0.1%
89.2 187
 
< 0.1%
31.09 187
 
< 0.1%
40.22 186
 
< 0.1%
75.2 183
 
< 0.1%
45.8 180
 
< 0.1%
57.67 180
 
< 0.1%
71.5 180
 
< 0.1%
88.23 180
 
< 0.1%
55.5 179
 
< 0.1%
Other values (6991) 998170
99.8%
ValueCountFrequency (%)
20 86
< 0.1%
20.01 143
< 0.1%
20.02 130
< 0.1%
20.03 150
< 0.1%
20.04 175
< 0.1%
20.05 147
< 0.1%
20.06 148
< 0.1%
20.07 138
< 0.1%
20.08 157
< 0.1%
20.09 138
< 0.1%
ValueCountFrequency (%)
90 61
 
< 0.1%
89.99 152
< 0.1%
89.98 143
< 0.1%
89.97 146
< 0.1%
89.96 157
< 0.1%
89.95 148
< 0.1%
89.94 152
< 0.1%
89.93 134
< 0.1%
89.92 146
< 0.1%
89.91 125
< 0.1%

Interactions

2025-08-11T16:47:03.517463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:06.037062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:13.506314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:22.839079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:32.029989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:39.989774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:47.286465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:55.107934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:03.454003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:11.128489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:19.375851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:27.167192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:35.727328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:43.955942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:54.302562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:04.432317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:06.500215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:14.068882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:23.500837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:32.520566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:40.499447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:47.747291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:55.612318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:04.029729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:11.643785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:19.863988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:27.705630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:36.335034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:44.711293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:54.925862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:05.422885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:07.034998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:14.616875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:24.031303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:33.017402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:41.046237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:48.227229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:56.119235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:04.527591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:12.228071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:20.393906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:28.254668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:36.827767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:45.489691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:55.733755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:06.572098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:07.551262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:15.155387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:24.581364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:33.485698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:41.565477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:48.696315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:56.627244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:05.184504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:12.941840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:20.882071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:28.820672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:37.306550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:46.514025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:56.363498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:07.181279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:08.135291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:15.706679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:25.160532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:33.931525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:42.012205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:49.326845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:57.104929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:05.695281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:13.667802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:21.376975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:29.386448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:37.773190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:47.095871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:56.924678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:07.710453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:08.632371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:16.187929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:25.730453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:34.451151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:42.492155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:49.815276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:57.579438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:06.173384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:14.260414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:21.858749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:29.975933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:38.243770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:47.736133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:57.452269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:08.236786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:09.116464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:16.688030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:26.335159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:34.966604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:42.983521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:50.299052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:58.099057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:06.628197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:14.773477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:22.496684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:30.554347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:38.756818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:48.603591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:57.919166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:08.741850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:09.600790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:17.175213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:26.901667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:35.497631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:43.475379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:50.729742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:58.563004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:07.061524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:15.261786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:22.943090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:31.143761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:39.227260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:49.204735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:58.375503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:09.183827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:10.053319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:17.746907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:27.615767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:36.034665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:43.931146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:51.196472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:45:59.015448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:07.545796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:15.741210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:23.440799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:31.891672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:39.672086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:49.852134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:45:37.167958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:45:59.957762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:46:51.208888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:47:10.535517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:46:02.908132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2025-08-11T16:46:18.880725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:26.643842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:35.241393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:43.162650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:46:53.604333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-11T16:47:02.553243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-08-11T16:47:37.337126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Age GroupAvailability of Vaccines/TreatmentAverage Treatment Cost (USD)CountryDALYsDisease CategoryDisease NameDoctors per 1000Education IndexGenderHealthcare Access (%)Hospital Beds per 1000Improvement in 5 Years (%)Incidence Rate (%)Mortality Rate (%)Per Capita Income (USD)Population AffectedPrevalence Rate (%)Recovery Rate (%)Treatment TypeUrbanization Rate (%)Year
Age Group1.0000.0000.0010.0000.0000.0020.0020.0000.0000.0000.0000.0010.0000.0000.0000.0010.0000.0000.0000.0000.0000.002
Availability of Vaccines/Treatment0.0001.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0030.0000.0000.0010.0000.0000.0000.004
Average Treatment Cost (USD)0.0010.0001.0000.003-0.0000.0000.0020.0000.0000.0000.000-0.0010.0010.000-0.002-0.0020.002-0.0010.0000.0000.0010.001
Country0.0000.0000.0031.0000.0010.0020.0000.0010.0020.0000.0000.0020.0000.0000.0020.0000.0010.0010.0010.0010.0000.001
DALYs0.0000.000-0.0000.0011.0000.0010.0010.001-0.0010.0000.000-0.0010.0000.000-0.0010.000-0.0020.0010.0000.000-0.0010.001
Disease Category0.0020.0000.0000.0020.0011.0000.0000.0010.0000.0020.0000.0000.0000.0000.0010.0010.0020.0000.0000.0000.0000.002
Disease Name0.0020.0000.0020.0000.0010.0001.0000.0000.0000.0030.0010.0000.0000.0010.0000.0000.0010.0010.0000.0010.0000.000
Doctors per 10000.0000.0010.0000.0010.0010.0010.0001.0000.0010.0000.0010.002-0.000-0.0000.0010.0010.0010.000-0.0010.001-0.000-0.000
Education Index0.0000.0000.0000.002-0.0010.0000.0000.0011.0000.0000.001-0.002-0.001-0.000-0.000-0.0010.001-0.000-0.0020.002-0.000-0.001
Gender0.0000.0000.0000.0000.0000.0020.0030.0000.0001.0000.0000.0010.0010.0000.0000.0000.0000.0010.0010.0000.0000.001
Healthcare Access (%)0.0000.0000.0000.0000.0000.0000.0010.0010.0010.0001.000-0.000-0.0010.0010.000-0.0010.000-0.0000.0020.0010.000-0.001
Hospital Beds per 10000.0010.000-0.0010.002-0.0010.0000.0000.002-0.0020.001-0.0001.0000.000-0.0000.0010.0010.0000.000-0.0010.001-0.001-0.001
Improvement in 5 Years (%)0.0000.0000.0010.0000.0000.0000.000-0.000-0.0010.001-0.0010.0001.000-0.0010.0000.001-0.0010.0000.0010.0010.001-0.001
Incidence Rate (%)0.0000.0000.0000.0000.0000.0000.001-0.000-0.0000.0000.001-0.000-0.0011.0000.0000.0010.0020.0000.0000.0000.000-0.001
Mortality Rate (%)0.0000.003-0.0020.002-0.0010.0010.0000.001-0.0000.0000.0000.0010.0000.0001.000-0.002-0.0020.0010.0010.0000.0000.000
Per Capita Income (USD)0.0010.000-0.0020.0000.0000.0010.0000.001-0.0010.000-0.0010.0010.0010.001-0.0021.000-0.0000.0010.0000.0000.0000.000
Population Affected0.0000.0000.0020.001-0.0020.0020.0010.0010.0010.0000.0000.000-0.0010.002-0.002-0.0001.0000.0010.0000.001-0.0000.001
Prevalence Rate (%)0.0000.001-0.0010.0010.0010.0000.0010.000-0.0000.001-0.0000.0000.0000.0000.0010.0010.0011.000-0.0000.0000.002-0.001
Recovery Rate (%)0.0000.0000.0000.0010.0000.0000.000-0.001-0.0020.0010.002-0.0010.0010.0000.0010.0000.000-0.0001.0000.0000.001-0.000
Treatment Type0.0000.0000.0000.0010.0000.0000.0010.0010.0020.0000.0010.0010.0010.0000.0000.0000.0010.0000.0001.0000.0000.002
Urbanization Rate (%)0.0000.0000.0010.000-0.0010.0000.000-0.000-0.0000.0000.000-0.0010.0010.0000.0000.000-0.0000.0020.0010.0001.0000.000
Year0.0020.0040.0010.0010.0010.0020.000-0.000-0.0010.001-0.001-0.001-0.001-0.0010.0000.0000.001-0.001-0.0000.0020.0001.000

Missing values

2025-08-11T16:47:12.677478image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-11T16:47:15.530559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryYearDisease NameDisease CategoryPrevalence Rate (%)Incidence Rate (%)Mortality Rate (%)Age GroupGenderPopulation AffectedHealthcare Access (%)Doctors per 1000Hospital Beds per 1000Treatment TypeAverage Treatment Cost (USD)Availability of Vaccines/TreatmentRecovery Rate (%)DALYsImprovement in 5 Years (%)Per Capita Income (USD)Education IndexUrbanization Rate (%)
0Italy2013MalariaRespiratory0.951.558.420-18Male47100757.743.347.58Medication21064No91.8244932.16168860.7986.02
1France2002EbolaParasitic12.468.638.7561+Male63431889.211.335.11Surgery47851Yes76.6523664.82806390.7445.52
2Turkey2015COVID-19Genetic0.912.356.2236-60Male15487856.414.073.49Vaccination27834Yes98.55415.81122450.4140.20
3Indonesia2011Parkinson's DiseaseAutoimmune4.686.293.990-18Other44622485.203.188.44Surgery144Yes67.3532012.22493360.4958.47
4Italy2013TuberculosisGenetic0.8313.597.0161+Male47290867.004.615.90Medication8908Yes50.0628326.93477010.5048.14
5Saudi Arabia2011DengueBacterial10.996.494.6461+Female47923498.413.500.62Therapy42671Yes93.174169.83295970.4656.50
6USA2013MalariaCardiovascular18.426.339.3361+Female28993190.033.163.31Surgery15579No92.8045350.89600270.7020.48
7Nigeria2007TuberculosisNeurological3.485.711.210-18Female39329675.600.543.54Medication15744Yes65.4545849.81232220.4666.49
8Italy2000RabiesChronic15.594.746.3819-35Female25311087.874.565.84Therapy7669Yes59.2322539.92308490.5541.27
9Australia2006CholeraChronic10.122.086.0061+Male17414395.904.636.01Medication9468Yes93.2146942.96688560.9083.30
CountryYearDisease NameDisease CategoryPrevalence Rate (%)Incidence Rate (%)Mortality Rate (%)Age GroupGenderPopulation AffectedHealthcare Access (%)Doctors per 1000Hospital Beds per 1000Treatment TypeAverage Treatment Cost (USD)Availability of Vaccines/TreatmentRecovery Rate (%)DALYsImprovement in 5 Years (%)Per Capita Income (USD)Education IndexUrbanization Rate (%)
999990Brazil2014RabiesParasitic5.643.901.0836-60Male60977595.321.247.57Vaccination38124No87.6910371.87169360.8268.51
999991India2005HIV/AIDSBacterial6.0913.682.9161+Male7392378.903.041.83Therapy14718Yes87.4632623.37416680.5846.53
999992UK2009EbolaChronic15.6210.853.7961+Female74783355.914.015.66Surgery42983Yes81.0635493.9186320.4828.04
999993Nigeria2006ZikaMetabolic16.1012.348.9461+Other12472399.600.501.89Medication21911No89.6611511.57770380.8187.64
999994Turkey2024EbolaAutoimmune6.454.063.4661+Male38071456.820.684.83Medication34450No53.5914176.30601890.5763.74
999995Saudi Arabia2021Parkinson's DiseaseInfectious4.564.839.650-18Female11933288.781.984.23Vaccination4528Yes92.1110243.88293350.7527.94
999996Saudi Arabia2013MalariaRespiratory0.261.760.560-18Female35492782.241.286.34Surgery20686No84.472027.95307520.4777.66
999997USA2016ZikaRespiratory13.4414.131.9119-35Other80791571.464.188.11Therapy18807No86.8133387.31628970.7246.90
999998Nigeria2020AsthmaChronic1.9614.564.9861+Female38589657.102.616.91Medication21033Yes62.1548063.82981890.5134.73
999999Indonesia2004AsthmaNeurological15.589.840.5136-60Other63277577.572.106.79Therapy505Yes77.2332411.11796400.4278.51